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Reduced clinical severity during 2022 Shanghai Spring epidemic of SARS-CoV-2 omicron BA.2 variant infection——an integrated account of virus pathogenicity and vaccination effectiveness

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National science Review 2024年第4期11卷 24-27页

作者： Xingyue Wu Yao Chen Kangli Cao Yao Shen Xueling Wu Yilin Yang Zhongshu Kuang Qingrun Li Zhenzhen Lu Yichen Jia Mian Shao Guorong Gu Xiangwei Wang Ye Yao Ying Wang Shaodie Chen Zhigao Yu Wei Wei Longfei Ding Lulu Lan Tianwen Gu Xiangyu Long Jian Sun Lingyu Xing Jiayuan Shen Yi Han Yue Luo Sucheng Mu Mengna Lin Xiaoyan Zhang Rong Zeng Jianqing Xu Guoping Zhao Lihong Huang Zhenju Song Department of Emergency Medicine Clinical Center for Bio-Therapy Department of Biostatistics and Department of UrologyShanghai Key Laboratory of Lung Inflammation and Injury Zhongshan Hospital Fudan University Department of Respiratory and Critical Care Medicine Shanghai Pudong Hospital Fudan University Department of Pulmonology Renji Hospital Shanghai Jiao Tong University School of Medicine Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai Public Health Clinical Center Fudan University Department of Biostatistics School of Public Health Key Laboratory of Public Health Safety of Ministry of Education and Shanghai Institute of Infectious Disease and Biosecurity School of Public Health Fudan University Shanghai Institute of Immunology Department of Immunology and Microbiology Shanghai Institute of Virology Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of EducationSchool of Medicine Shanghai Jiao Tong University State Key Laboratory of Genetic Engineering Fudan Microbiome CenterSchool of Life Sciences Fudan University Bio-Med Big Data Center Shanghai Institute of Nutrition and Health Chinese Academy of Sciences

The emergence of SARS-Co V-2 variant of concern omicron BA.2 led to high infection incidence rates as well as reduced severity of COVID-19 during the Shanghai Spring epidemic of 2022 [1]. We assessed the role of viral pathogenicity and the effectiveness of inactivated vaccines in a retrospective observational study including 10 258 confirmed COVID-19cases identified between April and June of 2022, from four COVID-19 designated hospitals located in two areas of Shanghai, namely the West Bund and the East Bund, where the populations’ demographic characteristics varied significantly (Table S1, Fig. S1).

关键词： sars-cov-2 effectiveness epidemic shanghai integrated spring clinical variant virus account

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Less is More: Reducing Overfitting in Deep Learning for EEG Classification 50

Less is More: Reducing Overfitting in Deep Learning for EEG ...

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50th Computing in Cardiology, CinC 2023

作者： Zhou, Songchi Geng, Shijia Li, Jun Zhang, Deyun Xie, Ziqian Cheng, Chuandong Hong, Shenda Shanghai Jiao Tong University Department of Bioinformatics and Biostatistics Shanghai China HeartVoice Medical Technology Hefei China College of Electronic Science and Engineering Jilin University Changchun China University of Texas Health Science Center HoustonTX United States The First Affiliated Hospital of Ustc Department of Neurosurgery Hefei China National Institute of Health Data Science Peking University Beijing China Institute of Medical Technology Health Science Center of Peking University Beijing China

ISBN: (纸本)9798350382525

Although most of the patients' recordings includes large scale long-term physiological time series, the patient-level quantity is relatively small, posing great challenges for machine learning methods. As part of the George B. Moody PhysioNet Challenge 2023, we aim to propose a series of Reducing Overfitting techniques in Deep learning for EEG (coined as RODE) in this scenario, for neurological recovery prognosis. RODE is a simple yet effective machine-learning method, which is mostly powered by generalizable deep-learning features. Specifically, we first pre-train a convolutional neural network on the segment level with a margin-based and mining-based loss, and extract deep features from it. Then we decrease the deep features' complexity using dimensionality reduction methods, which prove to be quite significant for reducing overfitting in deep learning. Finally, we combine the reduced features with static features in patient level, and put them into an ensemble model for classification. Our team, PKU_NIHDS, receives a Challenge score of 0.821 on the hidden validation set and 0.708 on the hidden test set. © 2023 CinC.

关键词： Deep learning

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Flexible parametrization of graph-theoretical features from individual-specific networks for prediction

arXiv

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arXiv 2023年

作者： Gregorich, Mariella Simpson, Sean L. Heinze, Georg Medical University of Vienna Center for Medical Data Science Institute of Clinical Biometrics Vienna Austria Department of Biostatistics and Data Science Wake Forest University School of Medicine Winston-SalemNC United States

Statistical techniques are needed to analyse data structures with complex dependencies such that clinically useful information can be extracted. Individual-specific networks, which capture dependencies in complex biological systems, are often summarized by graph-theoretical features. These features, which lend themselves to outcome modelling, can be subject to high variability due to arbitrary decisions in network inference and noise. Correlation-based adjacency matrices often need to be sparsified before meaningful graph-theoretical features can be extracted, requiring the data analysts to determine an optimal threshold.. To address this issue, we propose to incorporate a flexible weighting function over the full range of possible thresholds to capture the variability of graph-theoretical features over the threshold domain. The potential of this approach, which extends concepts from functional data analysis to a graph-theoretical setting, is explored in a plasmode simulation study using real functional magnetic resonance imaging (fMRI) data from the Autism Brain Imaging data Exchange (ABIDE) Preprocessed initiative. The simulations show that our modelling approach yields accurate estimates of the functional form of the weight function, improves inference efficiency, and achieves a comparable or reduced root mean square prediction error compared to competitor modelling approaches. This assertion holds true in settings where both complex functional forms underlie the outcome-generating process and a universal threshold value is employed. We demonstrate the practical utility of our approach by using resting-state fMRI data to predict biological age in children. Our study establishes the flexible modelling approach as a statistically principled, serious competitor to ad-hoc methods with superior performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Biomarker combination based on the Youden index with and without gold standard

arXiv

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arXiv 2024年

作者： Sun, Ao Li, Yanting Zhou, Xiao-Hua Center of Data Science Peking University Beijing China Rheumatology Department Guang’anmen Hospital Beijing China Department of Biostatistics Beijing International Center for Mathematical Research Peking University Beijing China

In clinical practice, multiple biomarkers are often measured on the same subject for disease diagnosis, and combining them can improve diagnostic accuracy. Existing studies typically combine multiple biomarkers by maximizing the Area Under the ROC Curve (AUC), assuming a gold standard exists or that biomarkers follow a multivariate normal distribution. However, practical diagnostic settings require both optimal combination coefficients and an effective cutoff value, and the reference test may be imperfect. In this paper, we propose a two-stage method for identifying the optimal linear combination and cutoff value based on the Youden index. First, it maximizes an approximation of the empirical AUC to estimate the optimal linear coefficients for combining multiple biomarkers. Then, it maximizes the empirical Youden index to determine the optimal cutoff point for disease classification. Under the semiparametric single index model and regularity conditions, the estimators for the linear coefficients, cutoff point, and Youden index are consistent. This method is also applicable when the reference standard is imperfect. We demonstrate the performance of our method through simulations and apply it to construct a diagnostic scale for Chinese medicine. Copyright © 2024, The Authors. All rights reserved.

关键词： Normal distribution

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Sex-Specific Differences in Thoracic Injury Prevalence and Causation: a CIREN database Investigation

Sex-Specific Differences in Thoracic Injury Prevalence and C...

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2024 International Research Council on the Biomechanics of Injury, IRCOBI 2024

作者： Devane, Karan S. Armstrong, William Hsu, Fang-Chi Miller, Anna N. Kiani, Bahram Martin, R. Shayn Stitzel, Joel D. Weaver, Ashley A. Center for Injury Biomechanics Wake Forest University School of Medicine Winston-SalemNC United States Departments of Biostatistics and Data Science Radiology and Trauma Surgery Wake Forest University School of Medicine Winston-SalemNC United States Department of Orthopedic Surgery Washington University School of Medicine St. LouisMO United States

The objective of the study was to identify and quantify sex-specific disparities in thoracic injury patterns and causation among seriously injured first-row occupants in frontal motor vehicle collisions (MVC) from the Crash Injury Research and Engineering Network (CIREN) database, years 2005-2022. The sample comprised front-seat CIREN occupants (ages 16+;54% female) with AIS 2+ thoracic injuries. Injuries were categorized into six types. The involved physical components (IPCs) were grouped for injury causation analysis into seven groups. Logistic regression analysis explored associations between injury types, IPC types, confounding variables, and sex. There were 1,952 AIS 2+ thoracic injuries sustained by the 966 occupants. Females had higher odds of heart injuries and lower odds of lung injuries. In females, the IPC attributed to the thoracic injury had higher odds of being the seatbelt and lower odds of being the instrument panel. The results of this study impact thoracic trauma prevention, emphasizing the need for detailed data on injury causation in MVCs. Identifying factors influencing thoracic injury outcomes can guide effective prevention and treatment strategies, stressing the importance of considering sex-specificity in vehicle safety system development and assessment. © 2024 International Research Council on the Biomechanics of Injury. All rights reserved.

关键词： Logistic regression

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LEARNING SPARSE GROUP MODELS THROUGH BOOLEAN RELAXATION 11

LEARNING SPARSE GROUP MODELS THROUGH BOOLEAN RELAXATION

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11th International Conference on Learning Representations, ICLR 2023

作者： Wang, Yijie Zhou, Yuan Huang, Xiaoqing Huang, Kun Zhang, Jie Ma, Jianzhu Computer Science Department Indiana University Bloomington United States Yau Mathematical Sciences Center Department of Mathematical Sciences Tsinghua University China Department of Biostatistics & Health Data Science Indiana University United States Department of Medical and Molecular Genetics Indiana University United States Institute for AI Industry Research Tsinghua University China

We introduce an efficient algorithmic framework for learning sparse group models formulated as the natural convex relaxation of a cardinality-constrained program with Boolean variables. We provide theoretical techniques to characterize the equivalent condition when the relaxation achieves the exact integral optimal solution, as well as a rounding algorithm to produce a feasible integral solution once the optimal relaxation solution is fractional. We demonstrate the power of our equivalent condition by applying it to two ensembles of random problem instances that are challenging and popularly used in literature and prove that our method achieves the exactness with overwhelming probability and the nearly optimal sample complexity. Empirically, we use synthetic datasets to demonstrate that our proposed method significantly outperforms the state-of-the-art group sparse learning models in terms of individual and group support recovery when the number of samples is small. Furthermore, we show the out-performance of our method in cancer drug response prediction. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Relaxation processes

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A semi-parametric estimation of personalized dose-response function using instrumental variables

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 4227-4264页

作者： Wei Luo Yeying Zhu Xuekui Zhang Lin Lin Center for Data Science Zhejiang University Hangzhou P.R.China Department of Statistics and Actuarial Science University of Waterloo Waterloo ON Canada Department of Mathematics and Statistics University of Victoria Victoria BC Canada Department of Biostatistics and Bioinformatics Duke University Durham NC

In the application of instrumental variable analysis that conducts causal inference in the presence of unmeasured confounding, invalid instrumental variables and weak instrumental variables often exist which complicate the analysis. In this paper, we propose a model-free dimension reduction procedure to select the invalid instrumental variables and refine them into lower-dimensional linear combinations. The procedure also combines the weak instrumental variables into a few stronger instrumental variables that best condense their information. We then introduce the personalized dose-response function that incorporates the subject's personal characteristics into the conventional dose-response function, and use the reduced data from dimension reduction to propose a novel and easily implementable nonparametric estimator of this function. The proposed approach is suitable for both discrete and continuous treatment variables, and is robust to the dimensionality of data. Its effectiveness is illustrated by the simulation studies and the data analysis of ADNI-DoD study, where the causal relationship between depression and dementia is investigated.

关键词： causal inference central mean subspace Mendelian randomization SCAD sufficient dimension reduction

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Adaptive Sequential Singular Spectrum Analysis: Effective Signal Extraction with Application to Heart Rate Signals Related to E-Cigarette Use

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data science in science 2024年第1期3卷 2383770页

作者： Yang, James J. Buu, Anne Department of Biostatistics and Data Science University of Texas Health Science Center Houston TX United States Department of Health Promotion and Behavioral Sciences University of Texas Health Science Center Houston TX United States

The Singular Spectrum Analysis (SSA) is a useful tool for extracting signals from noisy time series. However, the structural insights provided by SSA are significantly influenced by the choice of window length. While the conventional approach, recommending a larger window length, excels with short to moderately-sized time series, it becomes computationally burdensome for longer time series, potentially amplifying mean squared reconstruction errors. This study addresses this methodological gap by introducing an adaptive sequential SSA method that iteratively selects an optimal window length for efficient extraction of essential eigen-sequences (signals) with minimal reconstruction error. This proposed method is versatile, catering to both short-moderate and lengthy time series. Simulation studies demonstrate its efficacy in scenarios where observed data stem from the sum of two sinusoidal functions and noise. Real data analysis on 6-day heart rate data from a young adult e-cigarette user reveals a distinct clustering of vaping events in the scatter plot of the first and third eigen-sequences, indicating the potential of developing “digital biomarkers” for vaping behavior based on extracted eigen-sequences in future studies. In conclusion, the adaptive sequential SSA method offers a robust and flexible approach for signal extraction in diverse time series applications. © 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.

关键词： e-cigarette heart rate Singular spectrum analysis singular value decomposition

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Statistics, Machine Learning, and data science: A Historical Review and a Look to the Future

Statistics, Machine Learning, and Data Science: A Historical...

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IEEE International Conference on Research, Innovation and Vision for the Future

作者： Tru Cao Department of Biostatistics and Data Science The University of Texas Health Science Center at Houston School of Public Health Chair: Cuong Pham (Posts & Telecommunications Institute of Technology Vietnam

We are witnessing the beginning of a data driven era with the explosion of data, impact of data on our everyday lives, and advances of data processing methodology and technology. At this juncture, data science has emerged as an interdisciplinary field to deal with data for which statistics and machine learning are two key enablers. Originally, statistics and machine learning appear to have been developed in the different contexts of mathematics and computer science, respectively. data science has brought them together in which statistics focuses on mathematical foundations and methods, while machine learning is more on algorithms and automated data processing. First, this talk looks back on a timeline of the emergence of statistics, machine learning, data science, and related fields. Second, it reviews a chronology of the invention and context of some important statistical and machine learning methods. Third, it discusses relationships between statistics, machine learning, and data science. Finally, it addresses some challenges and overcoming ways of learning from big data.

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ADAPTIVE SHRINKAGE ESTIMATION FOR PERSONALIZED DEEP KERNEL REGRESSION IN MODELING BRAIN TRAJECTORIES

arXiv

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arXiv 2025年

作者： Tassopoulou, Vasiliki Shou, Haochang Davatzikos, Christos Center for AI and Data Science for Integrated Diagnostics University of Pennsylvania United States Department of Bioengineering University of Pennsylvania United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania United States

Longitudinal biomedical studies monitor individuals over time to capture dynamics in brain development, disease progression, and treatment effects. However, estimating trajectories of brain biomarkers is challenging due to biological variability, inconsistencies in measurement protocols (e.g., differences in MRI scanners) as well as scarcity and irregularity in longitudinal measurements. Herein, we introduce a novel personalized deep kernel regression framework for forecasting brain biomarkers, with application to regional volumetric measurements. Our approach integrates two key components: a population model that captures brain trajectories from a large and diverse cohort, and a subject-specific model that captures individual trajectories. To optimally combine these, we propose Adaptive Shrinkage Estimation, which effectively balances population and subject-specific models. We assess our model’s performance through predictive accuracy metrics, uncertainty quantification, and validation against external clinical studies. Benchmarking against state-of-the-art statistical and machine learning models—including linear mixed effects models, generalized additive models, and deep learning methods—demonstrates the superior predictive performance of our approach. Additionally, we apply our method to predict trajectories of composite neuroimaging biomarkers, which highlights the versatility of our approach in modeling the progression of longitudinal neuroimaging biomarkers. Furthermore, validation on three external neuroimaging studies confirms the robustness of our method across different clinical contexts. We make the code available at https://***/vatass/AdaptiveShrinkageDKGP. © 2025, CC BY.

关键词： Aerobraking

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